source: trunk/src/scripts_Laura/ARCTIC/Travail_CEN/choose_new_classified_points.py @ 55

Last change on this file since 55 was 54, checked in by lahlod, 10 years ago

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1#!/usr/bin/env python
2# -*- coding: utf-8 -*-
3import string
4import numpy as np
5import matplotlib.pyplot as plt
6from pylab import *
7from mpl_toolkits.basemap import Basemap
8from mpl_toolkits.basemap import shiftgrid, cm
9from netCDF4 import Dataset
10import arctic_map # function to regrid coast limits
11import cartesian_grid_test # function to convert grid from polar to cartesian
12import scipy.special
13import ffgrid2
14import map_ffgrid
15from matplotlib import colors
16from matplotlib.font_manager import FontProperties
17import map_cartesian_grid
18
19
20###############################
21# time period characteristics #
22###############################
23MONTH = np.array(['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12'])
24month = np.array(['JANUARY', 'FEBRUARY', 'MARCH', 'APRIL', 'MAY', 'JUNE', 'JULY', 'AUGUST', 'SEPTEMBER', 'OCTOBER', 'NOVEMBER', 'DECEMBER'])
25month_day = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
26M = len(month)
27
28
29########################
30# grid characteristics #
31########################
32x0 = -3000. # min limit of grid
33x1 = 2500. # max limit of grid
34dx = 40.
35xvec = np.arange(x0, x1+dx, dx)
36nx = len(xvec) 
37y0 = -3000. # min limit of grid
38y1 = 3000. # max limit of grid
39dy = 40.
40yvec = np.arange(y0, y1+dy, dy)
41ny = len(yvec)
42
43
44##################################################################################################################
45# We devide the loop in two steps :
46# - first loop concerns JANUARY, FEBRUARY, MARCH, APRIL, SEPTEMBER, OCTOBER, NOVEMBER, DECEMBER - use of AMSUA23GHz SPEC emissivity to seperate ice from no-ice zones
47# - second loop concerns MAY, JUNE, JULY, AUGUST - use of AMSUA89GHz SPEC emissivity to seperate ice from no_ice zones
48##################################################################################################################
49frequ = 89 # apply threshold on this frequency
50'''
51#open .dat file to stack data (see end of loop)
52data_classif = open ('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/sub_classification/AMSUA'+str(frequ)+'_data_classification_parameters_ice_no-ice_with_AMSUA23-and-30-spec_2009.dat', 'a')
53bin = 50
54'''
55
56
57# daily parameter (2D-array) on ARCTIC area
58emis_spec = np.zeros([M, ny, nx, 31], float)
59emis_lamb = np.zeros([M, ny, nx, 31], float)
60emis_diff = np.zeros([M, ny, nx, 31], float)
61emis_ratio = np.zeros([M, ny, nx, 31], float)
62
63# daily parameter (2D-array) on ARCTIC SEA ICE area
64daily_spec_ice = np.zeros([M, ny, nx, 31], float)
65daily_lamb_ice = np.zeros([M, ny, nx, 31], float)
66daily_diff_ice = np.zeros([M, ny, nx, 31], float)
67daily_ratio_ice = np.zeros([M, ny, nx, 31], float)
68
69'''
70# monthly mean parameter (1D-array) on ARCTIC SEA ICE area transformed into vector
71spec_vec = np.zeros([M, ny * nx], float)
72lamb_vec = np.zeros([M, ny * nx], float)
73diff_vec = np.zeros([M, ny * nx], float)
74ratio_vec = np.zeros([M, ny * nx], float)
75
76# histogram distribution (intensity of occurence) of parameter in SEA ICE area (1D-array, bins = 200)
77hist_vals_spec = np.zeros([M, bin], float)
78hist_vals_lamb = np.zeros([M, bin], float)
79hist_vals_diff = np.zeros([M, bin], float)
80hist_vals_ratio = np.zeros([M, bin], float)
81
82# histogram distribution (emissivity value) of parameter in SEA ICE area (1D-array, bins = 200)
83corresp_emis_spec = np.zeros([M, bin], float)
84corresp_emis_lamb = np.zeros([M, bin], float)
85corresp_emis_diff = np.zeros([M, bin], float)
86corresp_emis_ratio = np.zeros([M, bin], float)
87'''
88months1 = np.array([0, 1, 2, 3, 8, 9, 10, 11]) # use AMSUA 23GHz to delimit ice/no_ice for JANUARY, FEBRUARY, MARCH, APRIL, SEPTEMBER, OCTOBER, NOVEMBER, DECEMBER
89for imo in months1:
90    print 'month ' + month[imo]
91    ##################################################################################
92    # choice of AMSUA 23GHz delimitation ice/no_ice for the sub_classification study #
93    ##################################################################################
94    print 'open threshold file'
95    fichier_emis = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/cartesian_grid_map_ice_no-ice_' + str(month[imo]) + '2009_AMSUA23_spec_lamb_thresholds.nc', 'r', format='NETCDF3_CLASSIC')
96    spec_lim = fichier_emis.variables['spec_ice_area'][:]
97    #lamb_lim = fichier_emis.variables['lamb_ice_area'][:]
98    fichier_emis.close()
99    #########################################################
100    # application of AMSUA 23GHz delimitation to other data #
101    #########################################################
102    print 'open emissivity to sub_classify file'
103    ## data of emis SPEC, LAMB, DIFF(SPEC-LAMB)
104    fichier_e = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_monthly_data_lamb_spec_near_nadir_AMSUB' + str(frequ) + '_' + str(month[imo]) + '2009.nc', 'r', format='NETCDF3_CLASSIC')
105    day = fichier_e.variables['days'][:]
106    emis_spec[imo, :, :, 0 : month_day[imo]] = fichier_e.variables['e_spec'][:]
107    emis_lamb[imo, :, :, 0 : month_day[imo]] = fichier_e.variables['e_lamb'][:]
108    emis_diff[imo, :, :, 0 : month_day[imo]] = fichier_e.variables['e_spec_lamb'][:]
109    fichier_e.close()
110    # calculate emis ratio daily
111    for ijr in range (0, month_day[imo]):
112        for ilon in range (0, nx):
113            for ilat in range (0, ny):
114                emis_ratio[imo, ilat, ilon, ijr] = ((emis_lamb[imo, ilat, ilon, ijr] - emis_spec[imo, ilat, ilon, ijr]) / emis_spec[imo, ilat, ilon, ijr]) * 100.
115                if (isnan(spec_lim[ilat, ilon]) == True):
116                    daily_spec_ice[imo, ilat, ilon, ijr] = NaN
117                    daily_lamb_ice[imo, ilat, ilon, ijr] = NaN
118                    daily_diff_ice[imo, ilat, ilon, ijr] = NaN
119                    daily_ratio_ice[imo, ilat, ilon, ijr] = NaN
120                else:
121                    daily_spec_ice[imo, ilat, ilon, ijr] = emis_spec[imo, ilat, ilon, ijr]
122                    daily_lamb_ice[imo, ilat, ilon, ijr] = emis_lamb[imo, ilat, ilon, ijr]
123                    daily_diff_ice[imo, ilat, ilon, ijr] = emis_diff[imo, ilat, ilon, ijr]
124                    daily_ratio_ice[imo, ilat, ilon, ijr] = emis_ratio[imo, ilat, ilon, ijr]
125    '''
126    # ATTENTION : previous part of script has been modified ==> cannot be applied directly to this following part of script (modification of arrays and names....
127    print 'compute SPEC distribution'
128    ########
129    # SPEC #
130    ########
131    cs = reshape(spec_ice[imo, :, :], size(spec_ice[imo, :, :]))[nonzero(isnan(reshape(spec_ice[imo, :, :], size(spec_ice[imo, :, :]))) == False)]
132    spec_vec[imo, 0 : len(cs)] = cs
133    hist_vals_spec[imo, :] = hist(spec_vec[imo, 0 : len(cs)], bins = bin, normed = True, histtype='step')[0]
134    for ibin in range (0, bin):
135        corresp_emis_spec[imo, ibin] = mean(hist(spec_vec[imo, 0 : len(cs)], bins = bin, normed = True, histtype='step')[1][ibin : ibin + 2])
136    print 'compute LAMB distribution'
137    ########
138    # LAMB #
139    ########
140    cl = reshape(lamb_ice[imo, :, :], size(lamb_ice[imo, :, :]))[nonzero(isnan(reshape(lamb_ice[imo, :, :], size(lamb_ice[imo, :, :]))) == False)]
141    lamb_vec[imo, 0 : len(cl)] = cl
142    hist_vals_lamb[imo, :] = hist(lamb_vec[imo, 0 : len(cl)], bins = bin, normed = True, histtype='step')[0]
143    for ibin in range (0, bin):
144        corresp_emis_lamb[imo, ibin] = mean(hist(lamb_vec[imo, 0 : len(cl)], bins = bin, normed = True, histtype='step')[1][ibin : ibin + 2])
145    print 'compute DIFF distribution'
146    ########
147    # DIFF #
148    ########
149    cd = reshape(diff_ice[imo, :, :], size(diff_ice[imo, :, :]))[nonzero(isnan(reshape(diff_ice[imo, :, :], size(diff_ice[imo, :, :]))) == False)]
150    diff_vec[imo, 0 : len(cd)] = cd
151    hist_vals_diff[imo, :] = hist(diff_vec[imo, 0 : len(cd)], bins = bin, normed = True, histtype='step')[0]
152    for ibin in range (0, bin):
153        corresp_emis_diff[imo, ibin] = mean(hist(diff_vec[imo, 0 : len(cd)], bins = bin, normed = True, histtype='step')[1][ibin : ibin + 2])
154    print 'compute RATIO distribution'
155    #########
156    # RATIO #
157    #########
158    cr = reshape(ratio_ice[imo, :, :], size(ratio_ice[imo, :, :]))[nonzero(isnan(reshape(ratio_ice[imo, :, :], size(ratio_ice[imo, :, :]))) == False)]
159    ratio_vec[imo, 0 : len(cr)] = cr
160    hist_vals_ratio[imo, :] = hist(ratio_vec[imo, 0 : len(cr)], bins = bin, normed = True, histtype='step')[0]
161    for ibin in range (0, bin):
162        corresp_emis_ratio[imo, ibin] = mean(hist(ratio_vec[imo, 0 : len(cr)], bins = bin, normed = True, histtype='step')[1][ibin : ibin + 2])
163    ######################
164    # stack in .dat file #
165    ######################
166    print 'start stacking in .dat file'
167    #data_classif = open ('/net/argos/data/parvati/lahlod/ARCTIC/AMSUB_ice_class/sub_classification/AMSUB'+str(frequ)+'_data_classification_parameters_ice_no-ice_with_AMSUA23-spec_2009.dat', 'a')
168    for ii in range (0, bin):
169        data_classif.write(('%(months)10s    %(hist_vals_spec)10.5f    %(corresp_emis_spec)10.5f    %(hist_vals_lamb)10.5f    %(corresp_emis_lamb)10.5f    %(hist_vals_diff)10.5f    %(corresp_emis_diff)10.5f    %(hist_vals_rate)10.5f    %(corresp_emis_rate)10.5f    \n' % {
170                                            'months':month[imo],
171                                            'hist_vals_spec':hist_vals_spec[imo, ii],
172                                            'corresp_emis_spec':corresp_emis_spec[imo, ii],
173                                            'hist_vals_lamb':hist_vals_lamb[imo, ii],
174                                            'corresp_emis_lamb':corresp_emis_lamb[imo, ii],
175                                            'hist_vals_diff':hist_vals_diff[imo, ii],
176                                            'corresp_emis_diff':corresp_emis_diff[imo, ii],
177                                            'hist_vals_rate':hist_vals_ratio[imo, ii],
178                                            'corresp_emis_rate':corresp_emis_ratio[imo, ii],
179                                            }))
180    '''
181    ########################
182    # stack in netcdf file #
183    ########################
184    print 'stack in file month ' + str(month[imo])
185    rootgrp = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUB_ice_class/sub_classification/cartesian_grid_map_sea_ice_extent_with-AMSUA23-and-89_' + month[imo] + '2009_AMSUB' + str(frequ) + '_spec_thresholds.nc', 'w', format='NETCDF3_CLASSIC')
186    rootgrp.createDimension('longitude', nx)
187    rootgrp.createDimension('latitude', ny)
188    rootgrp.createDimension('days', month_day[imo])
189    nc_lon = rootgrp.createVariable('longitude', 'f', ('longitude',))
190    nc_lat = rootgrp.createVariable('latitude', 'f', ('latitude',))
191    nc_days = rootgrp.createVariable('days', 'f', ('days',))
192    nc_ice_spec = rootgrp.createVariable('spec_ice_area', 'f', ('latitude', 'longitude', 'days'))
193    nc_ice_lamb = rootgrp.createVariable('lamb_ice_area', 'f', ('latitude', 'longitude', 'days'))
194    nc_ice_diff = rootgrp.createVariable('diff_ice_area', 'f', ('latitude', 'longitude', 'days'))
195    nc_ice_ratio = rootgrp.createVariable('ratio_ice_area', 'f', ('latitude', 'longitude', 'days'))
196    nc_lon[:] = xvec
197    nc_lat[:] = yvec
198    nc_days[:] = np.arange(0, month_day[imo])
199    nc_ice_spec[:] = daily_spec_ice[imo, :, :, 0 : month_day[imo]]
200    nc_ice_lamb[:] = daily_lamb_ice[imo, :, :, 0 : month_day[imo]]
201    nc_ice_diff[:] = daily_diff_ice[imo, :, :, 0 : month_day[imo]]
202    nc_ice_ratio[:] = daily_ratio_ice[imo, :, :, 0 : month_day[imo]]
203    rootgrp.close()
204
205
206
207
208months2 = np.array([4, 5, 6, 7])# use AMSUA 89GHz to delimit ice/no_ice for MAY, JUNE, JULY, AUGUST
209for imo in months2:
210    print 'month ' + month[imo]
211    ##################################################################################
212    # choice of AMSUA 23GHz delimitation ice/no_ice for the sub_classification study #
213    ##################################################################################
214    print 'open threshold file'
215    fichier_emis = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/cartesian_grid_map_ice_no-ice_' + str(month[imo]) + '2009_AMSUA89_spec_lamb_thresholds.nc', 'r', format='NETCDF3_CLASSIC')
216    spec_lim = fichier_emis.variables['spec_ice_area'][:]
217    #lamb_lim = fichier_emis.variables['lamb_ice_area'][:]
218    fichier_emis.close()
219    #########################################################
220    # application of AMSUA 23GHz delimitation to other data #
221    #########################################################
222    print 'open emissivity to sub_classify file'
223    ## data of emis SPEC, LAMB, DIFF(SPEC-LAMB)
224    fichier_e = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_monthly_data_lamb_spec_near_nadir_AMSUB' + str(frequ) + '_' + str(month[imo]) + '2009.nc', 'r', format='NETCDF3_CLASSIC')
225    day = fichier_e.variables['days'][:]
226    emis_spec[imo, :, :, 0 : month_day[imo]] = fichier_e.variables['e_spec'][:]
227    emis_lamb[imo, :, :, 0 : month_day[imo]] = fichier_e.variables['e_lamb'][:]
228    emis_diff[imo, :, :, 0 : month_day[imo]] = fichier_e.variables['e_spec_lamb'][:]
229    fichier_e.close()
230    # calculate emis ratio daily
231    for ijr in range (0, month_day[imo]):
232        for ilon in range (0, nx):
233            for ilat in range (0, ny):
234                emis_ratio[imo, ilat, ilon, ijr] = ((emis_lamb[imo, ilat, ilon, ijr] - emis_spec[imo, ilat, ilon, ijr]) / emis_spec[imo, ilat, ilon, ijr]) * 100.
235                if (isnan(spec_lim[ilat, ilon]) == True):
236                    daily_spec_ice[imo, ilat, ilon, ijr] = NaN
237                    daily_lamb_ice[imo, ilat, ilon, ijr] = NaN
238                    daily_diff_ice[imo, ilat, ilon, ijr] = NaN
239                    daily_ratio_ice[imo, ilat, ilon, ijr] = NaN
240                else:
241                    daily_spec_ice[imo, ilat, ilon, ijr] = emis_spec[imo, ilat, ilon, ijr]
242                    daily_lamb_ice[imo, ilat, ilon, ijr] = emis_lamb[imo, ilat, ilon, ijr]
243                    daily_diff_ice[imo, ilat, ilon, ijr] = emis_diff[imo, ilat, ilon, ijr]
244                    daily_ratio_ice[imo, ilat, ilon, ijr] = emis_ratio[imo, ilat, ilon, ijr]
245    '''
246    # ATTENTION : previous part of script has been modified ==> cannot be applied directly to this following part of script (modification of arrays and names....
247    print 'compute SPEC distribution'
248    ########
249    # SPEC #
250    ########
251    cs = reshape(spec_ice[imo, :, :], size(spec_ice[imo, :, :]))[nonzero(isnan(reshape(spec_ice[imo, :, :], size(spec_ice[imo, :, :]))) == False)]
252    spec_vec[imo, 0 : len(cs)] = cs
253    hist_vals_spec[imo, :] = hist(spec_vec[imo, 0 : len(cs)], bins = bin, normed = True, histtype='step')[0]
254    for ibin in range (0, bin):
255        corresp_emis_spec[imo, ibin] = mean(hist(spec_vec[imo, 0 : len(cs)], bins = bin, normed = True, histtype='step')[1][ibin : ibin + 2])
256    print 'compute LAMB distribution'
257    ########
258    # LAMB #
259    ########
260    cl = reshape(lamb_ice[imo, :, :], size(lamb_ice[imo, :, :]))[nonzero(isnan(reshape(lamb_ice[imo, :, :], size(lamb_ice[imo, :, :]))) == False)]
261    lamb_vec[imo, 0 : len(cl)] = cl
262    hist_vals_lamb[imo, :] = hist(lamb_vec[imo, 0 : len(cl)], bins = bin, normed = True, histtype='step')[0]
263    for ibin in range (0, bin):
264        corresp_emis_lamb[imo, ibin] = mean(hist(lamb_vec[imo, 0 : len(cl)], bins = bin, normed = True, histtype='step')[1][ibin : ibin + 2])
265    print 'compute DIFF distribution'
266    ########
267    # DIFF #
268    ########
269    cd = reshape(diff_ice[imo, :, :], size(diff_ice[imo, :, :]))[nonzero(isnan(reshape(diff_ice[imo, :, :], size(diff_ice[imo, :, :]))) == False)]
270    diff_vec[imo, 0 : len(cd)] = cd
271    hist_vals_diff[imo, :] = hist(diff_vec[imo, 0 : len(cd)], bins = bin, normed = True, histtype='step')[0]
272    for ibin in range (0, bin):
273        corresp_emis_diff[imo, ibin] = mean(hist(diff_vec[imo, 0 : len(cd)], bins = bin, normed = True, histtype='step')[1][ibin : ibin + 2])
274    print 'compute RATIO distribution'
275    #########
276    # RATIO #
277    #########
278    cr = reshape(ratio_ice[imo, :, :], size(ratio_ice[imo, :, :]))[nonzero(isnan(reshape(ratio_ice[imo, :, :], size(ratio_ice[imo, :, :]))) == False)]
279    ratio_vec[imo, 0 : len(cr)] = cr
280    hist_vals_ratio[imo, :] = hist(ratio_vec[imo, 0 : len(cr)], bins = bin, normed = True, histtype='step')[0]
281    for ibin in range (0, bin):
282        corresp_emis_ratio[imo, ibin] = mean(hist(ratio_vec[imo, 0 : len(cr)], bins = bin, normed = True, histtype='step')[1][ibin : ibin + 2])
283    ######################
284    # stack in .dat file #
285    ######################
286    print 'start stacking in .dat file'
287    #data_classif = open ('/net/argos/data/parvati/lahlod/ARCTIC/AMSUB_ice_class/sub_classification/AMSUB'+str(frequ)+'_data_classification_parameters_ice_no-ice_with_AMSUA23-spec_2009.dat', 'a')
288    for ii in range (0, bin):
289        data_classif.write(('%(months)10s    %(hist_vals_spec)10.5f    %(corresp_emis_spec)10.5f    %(hist_vals_lamb)10.5f    %(corresp_emis_lamb)10.5f    %(hist_vals_diff)10.5f    %(corresp_emis_diff)10.5f    %(hist_vals_rate)10.5f    %(corresp_emis_rate)10.5f    \n' % {
290                                            'months':month[imo],
291                                            'hist_vals_spec':hist_vals_spec[imo, ii],
292                                            'corresp_emis_spec':corresp_emis_spec[imo, ii],
293                                            'hist_vals_lamb':hist_vals_lamb[imo, ii],
294                                            'corresp_emis_lamb':corresp_emis_lamb[imo, ii],
295                                            'hist_vals_diff':hist_vals_diff[imo, ii],
296                                            'corresp_emis_diff':corresp_emis_diff[imo, ii],
297                                            'hist_vals_rate':hist_vals_ratio[imo, ii],
298                                            'corresp_emis_rate':corresp_emis_ratio[imo, ii],
299                                            }))
300    '''
301    ########################
302    # stack in netcdf file #
303    ########################
304    print 'stack in file month ' + str(month[imo])
305    rootgrp = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUB_ice_class/sub_classification/cartesian_grid_map_sea_ice_extent_with-AMSUA23-and-89_' + month[imo] + '2009_AMSUB' + str(frequ) + '_spec_thresholds.nc', 'w', format='NETCDF3_CLASSIC')
306    rootgrp.createDimension('longitude', nx)
307    rootgrp.createDimension('latitude', ny)
308    rootgrp.createDimension('days', month_day[imo])
309    nc_lon = rootgrp.createVariable('longitude', 'f', ('longitude',))
310    nc_lat = rootgrp.createVariable('latitude', 'f', ('latitude',))
311    nc_days = rootgrp.createVariable('days', 'f', ('days',))
312    nc_ice_spec = rootgrp.createVariable('spec_ice_area', 'f', ('latitude', 'longitude', 'days'))
313    nc_ice_lamb = rootgrp.createVariable('lamb_ice_area', 'f', ('latitude', 'longitude', 'days'))
314    nc_ice_diff = rootgrp.createVariable('diff_ice_area', 'f', ('latitude', 'longitude', 'days'))
315    nc_ice_ratio = rootgrp.createVariable('ratio_ice_area', 'f', ('latitude', 'longitude', 'days'))
316    nc_lon[:] = xvec
317    nc_lat[:] = yvec
318    nc_days[:] = np.arange(0, month_day[imo])
319    nc_ice_spec[:] = daily_spec_ice[imo, :, :, 0 : month_day[imo]]
320    nc_ice_lamb[:] = daily_lamb_ice[imo, :, :, 0 : month_day[imo]]
321    nc_ice_diff[:] = daily_diff_ice[imo, :, :, 0 : month_day[imo]]
322    nc_ice_ratio[:] = daily_ratio_ice[imo, :, :, 0 : month_day[imo]]
323    rootgrp.close()
324
325'''
326data_classif.close()
327'''
328
329
330'''
331fichier = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/sub_classification/cartesian_grid_map_sea_ice_extent_with-AMSUA23-and-89_' + month[imo] + '2009_AMSUA' + str(frequ) + '_spec_thresholds.nc', 'r', format='NETCDF3_CLASSIC')
332ice_spec = fichier.variables['spec_ice_area'][:]
333ice_lamb = fichier.variables['lamb_ice_area'][:]
334ice_ratio = fichier.variables['ratio_ice_area'][:]
335fichier.close()
336mean_ratio = np.zeros([ny, nx], float)
337for ilon in range (0, nx):
338    for ilat in range (0, ny):
339        mean_ratio[ilat, ilon] = mean(ice_ratio[ilat, ilon, 0 : month_day[imo]][nonzero(isnan(ice_ratio[ilat, ilon, 0 : month_day[imo]]) == False)])
340
341
342ion()
343x_ind, y_ind, z_ind, volume = arctic_map.arctic_map_lat50()
344x_coast = x_ind
345y_coast = y_ind
346z_coast = z_ind
347map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, mean_ratio[:, :], -3., 5., 0.1, cm.s3pcpn_l_r, 'test')
348
349
350
351
352# test:
353ion()
354x_ind, y_ind, z_ind, volume = arctic_map.arctic_map_lat50()
355x_coast = x_ind
356y_coast = y_ind
357z_coast = z_ind
358for imo in range (0, M):
359    map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, ratio_ice[imo, :, :], 0., 4., 0.01, cm.s3pcpn_l_r, 'Sea ice extent - emissivity RATIO')
360    title('AMSUA ' + str(frequ) + ' - ' + str(month[imo]) + ' 2009')
361    plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/sub_classification/maps_sea_ice_extent/emiss_ratio_map_AMSUA'+str(frequ)+'_with_AMSUA23-and-30-spec_'+str(month[imo])+'_2009.png')
362'''
363
364
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